knitr::opts_chunk$set(echo = TRUE)
library(here)
library(readxl)
library(tidyverse)Overview
In this script, we’re going to discuss a topic not directly connected to the topics we’ve discussed so far, but one that is crucial to your development as a data scientist.
Every data analyst, regardless of skill level or experience, occasionally encounters a problem that they need help solving. This help could entail posting to an online forum like StackOverflow, posting to a social media website like Slack, or emailing friends or coworkers.
In these scenarios, it is crucial to create a minimally reproducible example - an example that allows your problem to be easily and quickly re-created by others. This ties into the larger issue of reproducibility, which you will read about this week.
When creating a minimally reproducible example, you should focus on simplicity. Others don’t necessarily need access to your whole dataset - just a dataset with the same properties as yours.
An example
The easiest way to discuss this problem is with an example of our own. We’re going to use the hotel bookings dataset, or hotel_bookings.csv. This comes to us from Jesse Mostipak on Kaggle. We’ll also filter the dataset to only include the cases where customers actually checked out.
hotel_bookings <- here("posts","_data","hotel_bookings.csv") %>%
read_csv() %>%
filter(str_detect(reservation_status,"Check-Out"))Rows: 119390 Columns: 32
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
date (1): reservation_status_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.hotel_bookingsThis dataset contains two different hotels, "Resort Hotel" and "City Hotel". There are also columns for market_segment (the type of guest that books the hotel) and distribution_channel (how they booked the reservation). Jesse’s key on Kaggle tells us that “TA” = “Travel Agent” and “TO” = “Tour Operators”.
distinct(hotel_bookings,market_segment)distinct(hotel_bookings,distribution_channel)Let’s say we want to know the proportion of bookings that come from all combinations of market_segment and distribution_channel within each hotel type. That is, we want to know these conditional probabilities.1
Let’s say that we don’t know how to do this in R. So, we decide to post to Slack for others to weigh in on. However, this is a large dataset. Even the filtered version of the dataset contains 75166 rows. We could post a screenshot, but this is not ideal based on reproducibility standards or practical considerations (how large the dataset is).
dput()
One solution is the dput() function. dput() takes as input an object currently in the environment, and produces code that can be copy-pasted to recreate the object exactly in any R environment.
dput(hotel_bookings)